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Microbiome–metabolite linkages drive greenhouse gas dynamics over a permafrost thaw gradient

Freire-Zapata, Viviana ; Holland-Moritz, Hannah ; Cronin, Dylan R. ; Aroney, Sam ; Smith, Derek A. ; Wilson, Rachel M. ; Ernakovich, Jessica G. ; Woodcroft, Ben J. ; Bagby, Sarah C. and Mondav, Rhiannon LU orcid , et al. (2024) In Nature Microbiology 9(11). p.2892-2908
Abstract

Interactions between microbiomes and metabolites play crucial roles in the environment, yet how these interactions drive greenhouse gas emissions during ecosystem changes remains unclear. Here we analysed microbial and metabolite composition across a permafrost thaw gradient in Stordalen Mire, Sweden, using paired genome-resolved metagenomics and high-resolution Fourier transform ion cyclotron resonance mass spectrometry guided by principles from community assembly theory to test whether microorganisms and metabolites show concordant responses to changing drivers. Our analysis revealed divergence between the inferred microbial versus metabolite assembly processes, suggesting distinct responses to the same selective pressures. This... (More)

Interactions between microbiomes and metabolites play crucial roles in the environment, yet how these interactions drive greenhouse gas emissions during ecosystem changes remains unclear. Here we analysed microbial and metabolite composition across a permafrost thaw gradient in Stordalen Mire, Sweden, using paired genome-resolved metagenomics and high-resolution Fourier transform ion cyclotron resonance mass spectrometry guided by principles from community assembly theory to test whether microorganisms and metabolites show concordant responses to changing drivers. Our analysis revealed divergence between the inferred microbial versus metabolite assembly processes, suggesting distinct responses to the same selective pressures. This contradicts common assumptions in trait-based microbial models and highlights the limitations of measuring microbial community-level data alone. Furthermore, feature-scale analysis revealed connections between microbial taxa, metabolites and observed CO2 and CH4 porewater variations. Our study showcases insights gained by using feature-level data and microorganism–metabolite interactions to better understand metabolic processes that drive greenhouse gas emissions during ecosystem changes.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
greenhouse gas emissions, permafrost, ecology, trait based model, microbiology
in
Nature Microbiology
volume
9
issue
11
pages
17 pages
publisher
Springer Nature
external identifiers
  • scopus:85205349343
  • pmid:39354152
ISSN
2058-5276
DOI
10.1038/s41564-024-01800-z
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s) 2024.
id
80364ffe-3200-4d11-be2f-ddfde19da87c
date added to LUP
2024-12-11 11:04:11
date last changed
2025-07-10 04:09:37
@article{80364ffe-3200-4d11-be2f-ddfde19da87c,
  abstract     = {{<p>Interactions between microbiomes and metabolites play crucial roles in the environment, yet how these interactions drive greenhouse gas emissions during ecosystem changes remains unclear. Here we analysed microbial and metabolite composition across a permafrost thaw gradient in Stordalen Mire, Sweden, using paired genome-resolved metagenomics and high-resolution Fourier transform ion cyclotron resonance mass spectrometry guided by principles from community assembly theory to test whether microorganisms and metabolites show concordant responses to changing drivers. Our analysis revealed divergence between the inferred microbial versus metabolite assembly processes, suggesting distinct responses to the same selective pressures. This contradicts common assumptions in trait-based microbial models and highlights the limitations of measuring microbial community-level data alone. Furthermore, feature-scale analysis revealed connections between microbial taxa, metabolites and observed CO<sub>2</sub> and CH<sub>4</sub> porewater variations. Our study showcases insights gained by using feature-level data and microorganism–metabolite interactions to better understand metabolic processes that drive greenhouse gas emissions during ecosystem changes.</p>}},
  author       = {{Freire-Zapata, Viviana and Holland-Moritz, Hannah and Cronin, Dylan R. and Aroney, Sam and Smith, Derek A. and Wilson, Rachel M. and Ernakovich, Jessica G. and Woodcroft, Ben J. and Bagby, Sarah C. and Mondav, Rhiannon and Hodgkins, Suzanne B. and Zayed, Ahmed A. and Varner, Ruth K. and Saleska, Scott R. and Ibba, Michael and Ferriere, Regis and Fahnestock, Maria Florencia and E. Cross, Jennifer and Rich, Virginia I. and Sullivan, Matthew B. and Stegen, James C. and Tfaily, Malak M.}},
  issn         = {{2058-5276}},
  keywords     = {{greenhouse gas emissions; permafrost; ecology; trait based model; microbiology}},
  language     = {{eng}},
  month        = {{11}},
  number       = {{11}},
  pages        = {{2892--2908}},
  publisher    = {{Springer Nature}},
  series       = {{Nature Microbiology}},
  title        = {{Microbiome–metabolite linkages drive greenhouse gas dynamics over a permafrost thaw gradient}},
  url          = {{http://dx.doi.org/10.1038/s41564-024-01800-z}},
  doi          = {{10.1038/s41564-024-01800-z}},
  volume       = {{9}},
  year         = {{2024}},
}